Central cloud server and edge devices assisted high speed low-latency wireless connectivity

ABSTRACT

A central cloud server includes a connectivity enhanced database that specifies a plurality of time-of-day specific uplink and downlink beam alignment-wireless connectivity relationships for a surrounding area of each of a plurality of edge devices. The central cloud server obtains sensing information from the plurality of edge devices, where the sensing information includes position information of the plurality of edge devices and weather information in the surrounding area of each of the plurality of edge devices. The central cloud server utilizes the weather information to determine changes in a performance state of each of the plurality of edge devices in servicing one or more user equipment in the surrounding area in different weather conditions. The central cloud server furthers causes one or more edge devices to select a beam configuration or a radiation pattern in accordance with the weather information for high performance communication.

CROSS-REFERENCE TO RELATED APPLICATIONS/INCORPORATION BY REFERENCE

This Patent Application makes reference to, claims priority to, claimsthe benefit of, and is a Continuation Application of U.S. patentapplication Ser. No. 17/449,991 filed on Oct. 5, 2021, which is aContinuation Application of U.S. Pat. No. 11,159,958 granted on Oct. 26,2021, and is hereby incorporated herein by reference in its entirety.

FIELD OF TECHNOLOGY

Certain embodiments of the disclosure relate to a wireless communicationsystem. More specifically, certain embodiments of the disclosure relateto a central cloud server, an edge device, and a method for the centralcloud server and edge devices assisted high speed low-latency wirelessconnectivity.

BACKGROUND

Wireless telecommunication in modern times has witnessed advent ofvarious signal transmission techniques and methods, such as use ofbeamforming and beam steering techniques, for enhancing capacity ofradio channels. Latency and the high volume of data processing areconsidered prominent issues with next generation networks, such as 5G.Currently, the use of edge computing in next generation networks, suchas 5G and upcoming 6G, is an active area of research and many benefitshas been proposed, for example, faster communication between vehicles,pedestrians, and infrastructure, and other communication devices. Forexample, it is proposed that close proximity of conventional edgedevices to user equipment (UEs) may likely reduce the response delayusually suffered by UEs while accessing the traditional cloud. However,there are many open technical challenges for a successful and practicaluse of edge computing in the modern networks, especially in 5G or theupcoming 6G environment.

In a first example, it is known that fast and efficient beam managementmechanism may be a key enabler in advanced wireless communicationtechnologies, for example, in millimeter wave (5G) or the upcoming 6Gcommunications, to achieve low latency and high data rate requirements.One major technical challenge of the mmWave beamforming is the initialaccess latency. During the initial access phase, a UE and or aconventional repeater device need to scan multiple beams to find asuitable beam for attachment, for example, using the standard beamsweeping operation in the initial access phase. This process mayintroduce considerable latency depending on the number of beams in abeam book and a baseband decoding hardware latency. Such latency becomeseven more critical for mobile systems (e.g., when UEs are in motion) inwhich the channel, and hence beams or base stations, such as a gNodeB(gNB), may be rapidly changing. For example, currently, an averagemmWave gNB handover time is on the order of 10-20 seconds, assumingabout 500 meter of cell radius and a UE (e.g., a vehicle or a UE in thevehicle) travelling at the speed of 50 miles per hour (MPH), which isnot desirable.

In a second example, Quality of Experience (QoE) is another open issue,which is a measure of a user's holistic satisfaction level with aservice provider (e.g., Internet access, phone call, or other carriernetwork-enabled services). The challenge is how to ensure a seamlessconnectivity as well as QoE without significantly increasinginfrastructure cost, which may be commercially unsustainable withpresent solutions.

In a third example, heterogeneity may be another issue, where many UEsmay use different interfaces, radio access technologies (3G, 4G, 5G, orupcoming 6G), computing technologies (e.g., hardware and operatingsystems) and even one or more carrier networks, to communicate with theedge cloud. Such heterogeneity in wireless communication may furtheraggravate the challenges in developing a solution that is portable,practical, and upgradable across different environment.

In yet another example, how to consider the dynamic nature ofsurroundings is another open issue, especially for next generationnetworks, such as mmWave communication, that may adversely impactreliability in provisioning of consistent high-speed low latencywireless connectivity. In certain scenarios, the known challenges ofmmWave, namely signal loss, poor reach, and easy blockage by moving orstationary objects in surroundings are amplified and uncertainty inachieving reliable wireless connectivity with QoE is increased as aresult of the dynamic nature of surroundings, which is not desirable.

Further limitations and disadvantages of conventional and traditionalapproaches will become apparent to one of skill in the art, throughcomparison of such systems with some aspects of the present disclosureas set forth in the remainder of the present application with referenceto the drawings.

BRIEF SUMMARY OF THE DISCLOSURE

A central cloud server, an edge device, and a method for the centralcloud server and edge devices assisted high speed low-latency wirelessconnectivity for high performance communication, substantially as shownin and/or described in connection with at least one of the figures, asset forth more completely in the claims.

These and other advantages, aspects, and novel features of the presentdisclosure, as well as details of an illustrated embodiment thereof,will be more fully understood from the following description anddrawings.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a network environment diagram illustrating various componentsof an exemplary communication system with a central cloud server and aplurality of edge devices, in accordance with an exemplary embodiment ofthe disclosure.

FIG. 2 is a block diagram illustrating different components of anexemplary central cloud server, in accordance with an embodiment of thedisclosure.

FIG. 3 is a block diagram illustrating different components of anexemplary edge device, in accordance with an embodiment of thedisclosure.

FIGS. 4A, 4B, and 4C illustrate exemplary scenarios for implementationof the communication system and method for central cloud server and edgedevices assisted high speed low-latency wireless connectivity, inaccordance with an embodiment of the disclosure.

FIGS. 5A and 5B collectively is a flowchart that illustrates a methodfor a central cloud server and edge devices assisted high speedlow-latency wireless connectivity for high performance communication, inaccordance with an embodiment of the disclosure.

FIG. 6 is a flowchart that illustrates a method for a central cloudserver and edge device assisted high speed low-latency wirelessconnectivity for high performance communication, in accordance with anembodiment of the disclosure.

DETAILED DESCRIPTION OF THE DISCLOSURE

Certain embodiments of the disclosure may be found in a central cloudserver, an edge device, and a method for the central cloud server andedge devices assisted high speed low-latency wireless connectivity forhigh performance communication. The central cloud server, the edgedevice, and the method of the present disclosure significantly reducesthe latency involved in initial access phase by making the edge devicesbypass the initial-access search. For example, the existing averagemmWave gNB handover time that is on the order of 10-20 seconds for amoving device, is significantly reduced by approximately 60-90%depending on the location, speed, and orientation of a user equipment(UE), such as a vehicle or a smartphone, using an intelligent databasethat is trained previously, and may be referred to as a connectivityenhanced database that specifies a plurality of time-of-day specificuplink and downlink beam alignment-wireless connectivity relationshipsfor a surrounding area of each of the plurality of edge devicesindependent of a plurality of different wireless carrier networks ofdifferent service providers. The central cloud server supports theplurality of different wireless carrier networks including differentinterfaces, radio access technologies, computing technologies (e.g.,hardware and operating systems) and is easily upgradable without anyneed to change the infrastructure. Thus, the central cloud server incoordination with the plurality of edge devices ensures a seamlessconnectivity as well as Quality of experience (QoE) withoutsignificantly increasing infrastructure cost. Moreover, the centralcloud server takes into account comprehensive sensing informationsurrounding each edge device. Thus, a dynamic nature of surroundings(e.g., any change in surroundings that has the potential to adverselyimpact signal propagation, cause signal loss, poor reach, or signalblockage by an object, such as a moving object or a stationary object,in the surroundings) is proactively handled and mitigated by the centralcloud server by distributing a different subset of information from theconnectivity enhanced database to each of the plurality of edge devices.Such distribution by the central cloud server may be done according to acorresponding position of the each of the plurality of edge devices thatenables easy handling and mitigation of any adverse impact on signalpropagation due to the dynamic nature of surroundings for consistenthigh-performance communication. In the following description, referenceis made to the accompanying drawings, which form a part hereof, and inwhich is shown, by way of illustration, various embodiments of thepresent disclosure.

FIG. 1 is a network environment diagram illustrating various componentsof an exemplary communication system with a central cloud server and aplurality of edge devices, in accordance with an exemplary embodiment ofthe disclosure. With reference to FIG. 1 , there is shown a blockdiagram 100 of a network environment that includes a central cloudserver 102, a plurality of edge devices 104, one or more user equipment(UEs) 106, and a plurality of base stations 108. There is further showna plurality of different wireless carrier networks (WCNs) 110, such as afirst WCN 110A of a first service provider and a second WCN 110B of asecond service provider.

The central cloud server 102 includes suitable logic, circuitry, andinterfaces that may be configured to communicate with the plurality ofedge devices 104, the one or more UEs 106, and the plurality of basestations 108. In an example, the central cloud server 102 may be aremote management server that is managed by a third party different fromthe service providers associated with the plurality of different WCNs110. In another example, the central cloud server 102 may be a remotemanagement server or a data center that is managed by a third party, orjointly managed, or managed in coordination and association with one ormore of the plurality of different WCNs 110. In an implementation, thecentral cloud server 102 may be a master cloud server or a mastermachine that is a part of a data center that controls an array of othercloud servers communicatively coupled to it, for load balancing, runningcustomized applications, and efficient data management.

Each edge device of the plurality of edge devices 104 includes suitablelogic, circuitry, and interfaces that may be configured to communicatewith the central cloud server 102. Each edge device of the plurality ofedge devices 104 may be one of an edge repeater device, a relay device,a small cell, a customer premise equipment (CPE), a road side unit (RSU)device, or a UE controlled by the central cloud server 102, or aninference server. In an example, the UE may be controlled out-of-band,for example, in a management plane, by the central cloud server 102. Inan implementation, some of the edge devices of the plurality of edgedevices 104 may be deployed at a fixed location while some may beportable. For example, an edge device may be a fixed wireless access(FWA) device, a repeater device, a small-cell, or even an inferenceserver (e.g., an edge cloud) deployed at a fixed location that covers agiven geographical area. In another example, some edge devices, such asan edge repeater device may be installed in a vehicle and thus locationof such edge repeater device may vary rapidly when the vehicle is inmotion. Moreover, some edge device may be portable, and thus theirlocation may change. In some implementation, an edge device may be apart of a telematics unit of a vehicle or implemented as a portablerepeater device.

Each of one or more UEs 106 may correspond to a telecommunicationhardware used by an end-user to communicate. Alternatively stated, theone or more UEs 106 may refer to a combination of a mobile equipment andsubscriber identity module (SIM). Each of the one or more UEs 106 may besubscriber of at least one of the plurality of different WCNs 110.Examples of the one or more UEs 106 may include, but are not limited toa smartphone, a vehicle, a virtual reality headset, an augment realitydevice, an in-vehicle device, a wireless modem, a customer-premisesequipment (CPE), a home router, a cable or satellite television set-topbox, a VoIP station, or any other customized hardware fortelecommunication.

Each of the plurality of base stations 108 may be a fixed point ofcommunication that may communicate information, in form of a pluralityof beams of RF signals, to and from communication devices, such as theone or more UEs 106 and the plurality of edge devices 104. Multiple basestations corresponding to one service provider, may be geographicallypositioned to cover specific geographical areas. Typically, bandwidthrequirements serve as a guideline for a location of a base station basedon relative distance between the plurality of UEs and the base station.The count of base stations depends on population density and geographicirregularities, such as buildings and mountain ranges, which mayinterfere with the plurality of beams of RF signals. In animplementation, each of the plurality of base stations 108 may be a gNB.In another implementation, the plurality of base stations 108 mayinclude eNBs, Master eNBs (MeNBs) (for non-standalone mode), and gNBs.

Each of the plurality of different WCNs 110 is owned, managed, orassociated with a mobile network operator (MNO), also referred to as amobile carrier, a cellular company, or a wireless service provider thatprovides services, such as voice, SMS, MMS, Web access, data services,and the like, to its subscribers, over a licensed radio spectrum. Eachof the plurality of different WCNs 110 may own or control elements of anetwork infrastructure to provide services to its subscribers over thelicensed spectrum, for example, 4G LTE, or 5G spectrum (FR1 or FR2). Forexample, the first base station 108A may be controlled, managed, orassociated with the first WCN 110A, and the second base station 108B maybe controlled, managed, or associated with the second WCN 110B differentfrom the first WCN 110A. The plurality of different WCNs 110 may alsoinclude mobile virtual network operators (MVNO).

Beneficially, the central cloud server 102 and the plurality of edgedevices 104 exhibit a decentralized model that not only brings cloudcomputing capabilities closer to UEs in order to reduce latency, butalso manifest several known benefits for various service providersassociated with the plurality of different WCNs 110. For example,reduces backhaul traffic by provisioning content at the edge,distributes computational resources geographically in differentlocations (e.g., on premise mini cloud, central offices, customerpremises, etc.,) depending on the use case requirements, and improvesreliability of a network by distributing content between edge devicesand the centralized cloud server 102. Apart from these and other knownbenefits (or inherent properties) of edge computing, the central cloudserver 102 improves and solves many open issues related to theconvergence of edge computing and modern wireless networks, such as 5Gor upcoming 6G. The central cloud server 102 significantly improves beammanagement mechanism of 5G new radio (NR), true 5G, and creates aplatform for upcoming 6G communications, to achieve low latency and highdata rate requirements. Based on the various information acquired fromthe plurality of edge devices 104 over a period of time, the centralcloud server 102 creates a connectivity enhanced database that specifiesa plurality of time-of-day specific uplink and downlink beamalignment-wireless connectivity relationships for a surrounding area ofeach of the plurality of edge devices 104 independent of the pluralityof different WCNs 110. This removes the complexity and substantiallyreduces the initial access latency as the standard beam sweepingoperation in the initial access phase is bypassed and is not required tobe performed at the end-user device or edge devices, which in turnimproves network performance of all associated WCNs of the plurality ofdifferent WCNs 110. The central cloud server 102 is able to handleheterogeneity in wireless communication in terms of differentinterfaces, radio access technologies (3G, 4G, 5G, or upcoming 6G),computing technologies (e.g., hardware and operating systems) and evenone or more carrier networks used by the one or more UEs 106. Moreover,the central cloud server 102 takes into account the dynamic nature ofsurroundings by use of the sensing information obtained from theplurality of edge devices 104 in real-time or near real time, toproactively avoid any adverse impact on reliability due to any suddensignal blockage or signal loss, thereby provisioning consistenthigh-speed low latency wireless connectivity. Thus, the central cloudserver 102 manifest higher QoE as compared to existing systems.

FIG. 2 is a block diagram illustrating different components of anexemplary central cloud server, in accordance with an embodiment of thedisclosure. FIG. 2 is explained in conjunction with elements from FIG. 1. With reference to FIG. 2 , there is shown a block diagram 200 of thecentral cloud server 102. The central cloud server 102 may include aprocessor 202, a network interface 204, and a primary storage 206. Theprimary storage 206 may further include sensing information 208 and beamalignment information 210. In an implementation, the primary storage 206may further include processing chain parameters 212. There is furthershown a machine learning model 214 and a connectivity enhanced database216.

In operation, there may be a training phase and an inference phase. Inthe training phase, the processor 202 may be configured to periodicallyobtain sensing information 208 from the plurality of edge devices 104.Each of the plurality of edge devices 104 may be deployed at differentlocations. For example, each of a first set of edge devices of theplurality of edge devices 104 may be an edge repeater device deployed ata corresponding fixed location to provide a non-line-of-sight (NLOS)transmission path between one or more base stations of the plurality ofthe base station 108 and one or more UEs, such as the one or more UEs106. Similarly, each of a second set of edge devices of the plurality ofedge devices 104 may be an edge device mounted on a vehicle, and thusits location may change rapidly when a corresponding vehicle on whichthe edge device is installed is in motion. In yet another example, someof the edge devices of the plurality of edge devices 104 may be UEscontrolled by the central cloud server 102. The plurality of edgedevices 104 may periodically sense its surroundings and communicate thesensed information, such as the sensing information 208, to the centralcloud server 102. The machine learning model 214 of the central cloudserver 102 may be periodically (e.g. daily and for differenttimes-of-day) trained on data points that are uploaded to the centralcloud server 102 from the plurality of edge devices 104.

In accordance with an embodiment, the sensing information 208 maycomprise a position of each of the plurality of edge devices 104, alocation of the one or more UEs 106 in the motion state or in thestationary state in the surrounding area of each of the plurality ofedge devices 104, a moving direction of different UEs (such as the oneor more UEs 106), a time-of-day, traffic information, road information,construction information, and traffic light information. The centralcloud server 102 obtains such sensing information 208 and stores thedata points of such sensing information 208 as input features. As thesensing information 208 is obtained periodically from various edgedevices of the plurality of edge devices 104, all changes in thesurroundings of each edge device is adequately captured and relayed tothe central cloud server 102.

In accordance with an embodiment, the processor 202 may be furtherconfigured to generate supplementary information as insights based on across-correlation of data points of the obtained sensing information208. When such data points of the sensing information 208 arecross-correlated with each other, supplementary information may bederived as insights by the central cloud server 102. For example, whentraffic information of a surrounding area of the first edge device 104Ahaving a first position is correlated with surrounding information atdifferent times-of-day over a period of time, the processor 202 of thecentral cloud server 102 may be configured to determine a trend and aload associated with the first edge device 104A (and similarly for otheredge devices) that may indicate an average number of UEs expected to beserviced by the first edge device 104A at different times-of-day, one ormore peak load time periods, one or more off-peak time periods. Theprocessor 202 may be further configured to determine how many edgedevices are active or not active, which edge devices may be employed toincrease the coverage and data throughput and reduce latency, and thelike.

In another example, more supplementary information may be derived asinsights taking into account traffic information, road information,construction information, and traffic light information, and othersensed information. Each edge device of the plurality of edge devices104 may use its own sensing mechanism, such as a sensing radar, to senseits surrounding environment and map its surrounding three-dimensional(3D) environment to generate a 3D environmental representation. The 3Denvironmental representation may indicate movable and immobile physicalstructures in the surrounding area of each of the plurality of edgedevices 104. In some implementations, each edge device of the pluralityof edge devices 104 may be configured to utilize external sensingdevices, such as Lidar, camera, accelerometer, Global NavigationSatellite System (GNSS), gyroscope, or Internet-of-Things (IoT) devices(e.g. video surveillance devices, roadside sensor systems for measuringspeed, local road conditions, local traffic, and the like) locatedwithin its communication range to acquire sensing information 208 fromsuch external devices. For example, an edge device may be an edgerepeater device mounted on a vehicle and communicatively coupled todifferent in-vehicle sensors via an in-vehicle network, so as to acquirethe sensing information 208 from such in-vehicle sensors (i.e. theexternal sensors) in real time or near time.

In accordance with an embodiment, the sensing information 208 mayfurther comprise a distance of each of the plurality of edge devices 104from the one or more UEs 106 and other movable and immobile physicalstructures in the surrounding area of each of the plurality of edgedevices 104. In an implementation, the distance of each of the pluralityof edge devices 104 from one or more UEs within its range, such as theone or more UEs 106, and other movable and immobile physical structuresin the surrounding area of each of the plurality of edge devices 104,may be determined at each of the plurality of edge devices 104 or atleast some edge devices of the plurality of edge devices 104, and thencommunicated to the central cloud server 102 as the sensing information208. In some implementations, the central cloud server 102 may beconfigured to determine such distance based on the position informationreceived from the plurality of edge devices 104. Additionally, theprocessor 202 of the central cloud server 102 may be configured tocross-correlate the distances using the generated 3D environmentalrepresentation for a given surrounding area of a given edge device forhigher accuracy.

In an example, the processor 202 may be further configured to determinedistance of each edge device (e.g. an edge repeater device) from itssurrounding objects, such as other vehicles, buildings, or edges of abuilding, distance of one or more serving base stations of the pluralityof base stations, trees, and other immobile physical structures (such asreflective objects) or other mobile objects. Moreover, Lidar informationfrom vehicles, information from a navigation system (such as maps, forexample, identifying cross-sections of streets), satellite imagery ofbuildings of a surrounding area, bridges, any signal obstruction from achange in construction structure etc., may be stored in the cloud, suchas the central cloud server 102.

The machine learning model 214 of the central cloud server 102 may beperiodically (e.g. daily and for different times of day) updated on suchdata points in real time or near time. The central cloud server 102 maybe further configured to cause the machine learning model 214 to findcorrelation among such data points to be used for a plurality ofpredictions and formulate rules to establish, maintain, and select oneor more edge devices in advance for various traffic scenarios to serveUEs and to identify improved (e.g., optimal) signal transmission pathsto reach to UEs and for efficient handover for a wireless connectivityat a later stage (i.e., in the inference phase). Based on the sensinginformation 208 obtained from the plurality of edge devices 104, theprocessor 202 may be further configured to detect where reflectiveobjects are located and used that information in radiation pattern ofthe RF signals, such as 5G signals. The sensing information 208 may beused to make radiation pattern that is correlated to areas such that thecommunicated RF signals are not reflected back. This means that when oneor more beams of RF signals are communicated from the plurality of edgedevices 104, comparatively significantly lower or almost negligible RFsignals are reflected back to the plurality of edge devices 104. Thelocation of the reflective objects and the correlation of the areasassociated with reflective objects with the radiation pattern to designenhanced or most suited beam configurations may be further used by theprocessor 202 to formulate rules for later use.

In accordance with an embodiment, the sensing information 208 mayfurther comprise weather information. The processor 202 may be furtherconfigured to utilize the weather information to determine one or morechanges in a performance state of each of the plurality of edge devices104 in servicing the one or more UEs 106 in its surrounding area indifferent weather conditions. It is known that more attention isprovided in the region between 30-300 GHz frequencies due to the largebandwidth which is available in this region to enable the plurality ofdifferent WCNs 110 to cope with the increasing demand for higher datarates and ultra-low latency services. However, the signals atfrequencies above 30 GHz may not propagate for long distances as thosebelow 30 GHz. Moreover, there is signal attenuation due to weatherfactors, such as humidity, rain, ice, and even there is a differenceobserved during summer and winter on the signal power level. Forexample, the signal loss difference between winter and summer for 28 GHzmay be about 1 dB, about 2 dB for 37 GHz, about 4 dB for 60 GHz. Lossesmay increase with frequency and distance. The processor 202 utilizessuch weather information to determine one or more changes in aperformance state of each of the plurality of edge devices 104 inservicing the one or more UEs 106 in its surrounding area in differentweather conditions, and accordingly may learn a correlation betweendifferent weather condition and signal power level and other performancestate of each of the plurality of edge devices 104 in servicing the oneor more UEs 106 in its surrounding area. Accordingly, the processor 202may be further configured to formulate rules to establish, maintain, andselect one or more edge devices in advance to mitigate signal losses invarious weather conditions to serve UEs and to identify improved (e.g.,optimal) signal transmission paths to reach to UEs via the edge devicesat a later stage (i.e., in the inference phase). For example, theprocessor 202 may be further configured to cause the one or more edgedevices to select a most appropriate beam configurations or radiationpattern in real time or near real time in accordance with the weathercondition obtained as a part of the sensing information 208 (i.e., inthe inference phase).

The processor 202 may be further configured to periodically obtain beamalignment information 210 from the plurality of edge devices 104. Thebeam alignment information 210 may be obtained and stored for theplurality of different WCNs 110. In an implementation, the beamalignment information 210 received by the central cloud server 102 fromthe plurality of edge devices 104 during the training phase may compriseone or more of a transmit (Tx) beam information, a receive (Rx) beaminformation, a Physical Cell Identity (PCID), and an absoluteradio-frequency channel number (ARFCN), and a signal strengthinformation associated with each of Tx beam and the Rx beam of theplurality of edge devices 104.

The processor 202 may be further configured to correlate the obtainedsensing information 208 and the beam alignment information 210 fordifferent times-of-day such that the connectivity enhanced database 216is generated that specifies a plurality of time-of-day specific uplinkand downlink beam alignment-wireless connectivity relationships for asurrounding area of each of the plurality of edge devices 104independent of the plurality of different WCNs 110. The correlationindicates that for a given set of input features extracted from thesensing information 208, what is the most suitable (i.e. best) initialaccess information for a given edge device according to its position toservice one or more UEs in its surrounding area such that a high-speedand low latency wireless connectivity can be achieved with increasedconsistency for different times-of-day. The connectivity enhanceddatabase 216 may be a low-latency database, for example, “DynamoDB”,“Scylla”, or other proven and known low-latency databases that canhandle one or more million transactions per second on a single cloudserver. The time-of day specific uplink beam-alignment-wirelessconnectivity relation specifies, for the given set of input features fora given time-of-day, which beam index to set at an edge device for theuplink communication, a specific Physical Cell Identity (PCID) whichindicates which gNB to connect to, or which WCN to select, whichspecific beam configuration to set, or whether a connection to the basestation is to be established directly or indirectly in a NLOS path usinganother edge device (e.g. another edge repeater device) in a network ofedge devices depending on the current location of the edge device.Similarly, the time-of day specific downlink beam-alignment-wirelessconnectivity relation specifies, for the given set of input features fora given time-of-day, which beam index to set at an edge device for thedownlink communication, which WCN to select, which specific beamconfiguration to set, what power level of the RF signal may besufficient, or an expected time period to service one or more UEs, suchas the first UE 106A, depending on the current location of the edgedevice. Thus, as the set of input features changes, the initial accessinformation also changes for the given edge device according to changedset of input features to continue servicing the one or more UEs, such asthe first UE 106A, in its surrounding area without any drop in QoE.Moreover, as the plurality of time-of-day specific uplink and downlinkbeam alignment-wireless connectivity relationships for the surroundingarea of each of the plurality of edge devices 104 is independent of theplurality of different WCNs 110, the complexity and the initial accesslatency is significantly reduced as the standard beam sweeping operationin the initial access phase is bypassed and is not required to beperformed at the end-user device or edge devices, which in turn improvesnetwork performance of associated WCNs of the plurality of differentWCNs 110. Furthermore, this way a consumer, such as the first UE 106A,is provided with the capability to choose which WCN (i.e. which serviceprovider) they like to connect to, and this is enabled from the cloud,such as the central cloud server 102. The processor 202 may beconfigured to transfer such specific initial access informationassociated with a WCN, such as the first WCN 110A to the edge device,such as the first edge device 104A, where such specific initial accessinformation is used by the edge device to establish wirelessconnectivity by passing conventional initial-access search. Thus, aconsumer with a UE, such as the first UE 106A, subscribed to the firstWCN 110A can request the edge device, such as the first edge device104A, to relay an RF signal of the first WCN 110A, and if the consumerwith the UE, such as the first UE 106A, is subscribed to the second WCN110B can request the edge device, such as the first edge device 104A, torelay an RF signal of the second WCN 110B.

In an implementation, the processor 202 may be further configured toextract and tag parameters of the beam alignment information 210 aslearning labels. The obtained sensing information 208 may be consideredas input features, whereas the beam alignment information 210 may beconsidered as learning labels for the correlation. The processor 202 maybe further configured to execute a mapping of the learning labels withone or more features of the obtained sensing information 208 until theplurality of time-of-day specific uplink and downlink beamalignment-wireless connectivity relationships is established for thesurrounding area of each of the plurality of edge devices 104. In animplementation, a machine learning algorithm, for example, an artificialneural network algorithm, may be used at the beginning before trainingwith the real-world training data of input features and parameters ofthe beam alignment information 210 as supervised learning labels. Whenthe machine learning algorithm is passed through the training data ofcorrelated input features and parameters of the beam alignmentinformation 210, the machine learning algorithm determines patterns suchthat the input features (e.g. distance of edge device with a UE, weathercondition, a UE location, moving direction, time-of-day, etc.) aremapped to the learning labels (e.g., best initial access information,such as best PCID, best beam index to be used, signal strengthmeasurement of a Tx/Rx beam, beam configuration, best transmission path,an absolute radio-frequency channel number (ARFCN) etc.). Since themachine learning model 214 is trained periodically, so if the basestation (e.g. a gNB) configuration is changed (e.g., a new sector or gNBis added or the PCID, ARFCN is changed) the machine learning model 214quickly adapts to the change. The processor 202 is further configured tocause the machine learning model 214 to assign more weight to recentdata points using, for example, an exponential time decay process. In anexample, the hyperparameters of the machine learning model 214 may beset and tuned depending on the formulated rules, and boundaries orlimits observed based on some early training. Some examples of thehyperparameters that may be set and observed in early learning and maybe tuned accordingly, may include a number of layers, layers dimensions,learning rate, and dropout regularization, and others regularizationrates. The machine learning model 214 may be a learned model generatedas output in the training process, and thus, over a period of time, themachine learning model 214 is able to predict the specific initialaccess information most suited for a given set of input features.Alternatively, in another implementation a convolutional neural network(CNN) may be used for deep learning, where the input features of thesensing information 208 and their relationship with the desired outputvalues may be derived automatically.

Thus, at the end of the training phase, the connectivity enhanceddatabase 216 is generated that specifies the plurality of time-of-dayspecific uplink and downlink beam alignment-wireless connectivityrelationships for the surrounding area of each of the plurality of edgedevices 104 independent of the plurality of different WCNs 110.Thereafter, the processor 202 may be further configured to distribute adifferent subset of information from the connectivity enhanced database216 to each of the plurality of edge devices 104 according to acorresponding position of the each of the plurality of edge devices 104.The different subset of information may cause each of the plurality ofedge devices 104 to service one or more UEs 106 in a motion state or ina stationary state in its surrounding area independent of the pluralityof different WCNs 110 and bypassing an initial access-search on thecorresponding edge device, such as the first edge device 104A. In theinference phase or the operational phase, whenever one or more UEsarrive in a later stage, instead of conducting an initial access-searchon an edge device, the central cloud server 102 assists the edge deviceby providing them with optimized initial access information (e.g., bestbeam index, best beam configuration, best ARFCN, and PCID) that it haslearned the machine learning model 214 during the training phase.Moreover, as the different subset of information from the connectivityenhanced database 216 is distributed in advance to each of the pluralityof edge devices 104 according to the corresponding position of the eachof the plurality of edge devices 104, each of the edge devices of theplurality of edge devices themselves may be able to identify theoptimized initial access information much faster than standard initialaccess procedure. Such subset of information is updated in real time ornear time whenever there is a change in the surrounding environment thatmay potentially affect signal propagation from the corresponding edgedevices of the plurality of edge devices 104.

In an example, in a city, there may be thousands of edge devices, whereeach edge device may only require enhanced information of itssurrounding area to execute high performance communication, for example,in order to increase data throughput (e.g., in multi-gigabit data rate),optimize signal propagation paths in uplink and downlink communication,reduce latency, handle heterogeneity and multiple WCNs, and improve QoE.Thus, the processor 202 of the central cloud server 102 sends only asubset of information specific to the given edge device, such as thefirst edge device 104A, from the connectivity enhanced database 216. Inan implementation, the subset of information specific to the given edgedevice includes time-of-day specific uplink and downlink beamalignment-wireless connectivity relationships only for a currentsurrounding area of the given edge device, such as the first edge device104A, as per current position of the given edge device. In someimplementation, the subset of information specific to the given edgedevice includes time-of-day specific uplink and downlink beamalignment-wireless connectivity relationships for a current surroundingarea (N), a previous surrounding area (N−1) in vicinity, and a nextsurrounding area (N+1) of the given edge device, such as the first edgedevice 104A, as per current position of the given edge device. In otherwords, the subset of information specific to the given edge deviceincludes optimized initial access information of at least threeconsequent geographical areas, where the middle geographical area may bethe surrounding area of the given edge device. This further improves aswitchover of a UE from one edge device (e.g. a deployed repeaterdevice) to another edge device (e.g. another deployed repeater device)to maintain consistent connectivity, high data throughput, and lowlatency communication as the UE moves from one geographical area toanother geographical area, where the switchover is controlled by thecentral cloud server 102.

In some implementations, some edge devices of the plurality of edgedevices 104 may be UEs controlled by the central cloud server 102. Insuch a case, the different subset of information causes one or more edgedevices of the plurality of edge devices 104 in a motion state or in astationary state to attach to a corresponding base station bypassing theinitial access-search on the corresponding edge device when thecorresponding edge device itself is the UE controlled by the centralcloud server 102.

In accordance with an embodiment, the processor 202 may be furtherconfigured to determine, based on position information of the first edgedevice 104A, whether a handover is required, and if so communicatewireless connectivity enhanced information including a specific initialaccess information to the first edge device 104A to bypass the initialaccess-search on the first edge device 104A. In a case where a wirelessconnection (e.g., a cellular connectivity) of a UE that is in motion,such as the first UE 106A, is about to become less than a thresholdperformance value, such performance drop may be predicted by the centralcloud server 102 based on new sensing information received from one ormore edge devices in the vicinity of the UE or from the UE itself. Forexample, the UE may be attached to the first base station 108A, and asthe UE moves, the distance from the first base station 108A mayincrease, and the signal strength may gradually decrease. Thus, based oninput features obtained from the new sensing information, such as amoving direction of the UE, a position of the UE, distance from one ormore edge devices in the vicinity of the UE, a current weathercondition, the location of the reflective objects around the UE, and anoverall 3D environment representation around the UE, the processor 202determines that a handover is required to maintain QoE, and accordinglyselects a suitable edge device (e.g. the first edge device 104A) amongthe plurality of edge devices 104 and communicates wireless connectivityenhanced information to such selected edge device so that there is noneed to perform beam sweeping operation or standard initial accesssearch on such edge device. Thus, the UE may readily connect to the edgedevice, and continue to perform uplink and downlink communication withhigh throughput without any interruptions. Similarly, in accordance withan embodiment, the processor 202 may be further configured to determinethat no handover is required for the first edge device 104A when aperformance state of a wireless connection of the UE, such as the firstUE 106A, is greater than a threshold performance value.

Alternatively, in an implementation, the processor 202 may be furtherconfigured to obtain processing chain parameters 212 from the pluralityof edge devices 104. In an implementation, the processing chainparameters 212 may be additional parameters treated as learning labels(e.g., supervised learning labels or unsupervised output values) inaddition to the beam alignment information. In another implementation,the processing chain parameters 212 may be received instead of the beamalignment information as the data obtained from processing chainparameters 212 may be a superset that includes the data points of thebeam alignment information. In yet another implementation, forprocessing purposes, the processing chain parameters 212 may be treatedand processed similar to that of the beam alignment information. Theprocessing chain parameters 212 may be obtained for further exhaustivetraining and inference of the machine learning model 214.

The processing chain parameters 212 includes information associated withelements of one or more cascaded receiver chains and one or morecascaded transmitter chains of each edge device, radio blocksinformation, and modem information of the plurality of edge devices 104.The central cloud server 102 may be configured such that it has accessto certain defined elements or all elements of one or more signalprocessing chain of each of the plurality of edge devices 104. Forexample, each of an uplink RF signal processing chain and a downlink RFsignal processing chain may include a cascading receiver chain forsignal reception, which includes elements, such as a set of low noiseamplifiers (LNA), a set of receiver front end phase shifters, and a setof power combiners. Similarly, each of the uplink RF signal processingchain and the downlink RF signal processing chain may further include acascading transmitter chain for baseband signal processing or digitalsignal processing for signal transmission, which includes elements suchas a set of power dividers, a set of phase shifters, a set of poweramplifiers (PA). There may be other elements and circuits like mixers,phase locked loops (PLL), frequency up-converters, frequencydown-converters, a filter bank that may include one or more filters,such as filters for channel selection or other digital filters for noisecancellation or reduction. The central cloud server 102 may beconfigured to securely access, monitor, and configure the informationassociated with such elements of one or more cascaded receiver chainsand one or more cascaded transmitter chains of each edge device tooptimize each radio blocks and overall radio frequency signals, such as5G signals.

In a first example, the central cloud server 102 may remotely accesselements of the one or more signal processing chains, like the set ofphase shifters, and utilize that, for example, to train the machinelearning model 214, and optimize every block of a RF signal includingphase (e.g. can control the phase shifting) etc. In a second example,the central cloud server 102 may remotely access information associatedwith elements, such as a set of LNAs to train the machine learning model214, and utilize that information, for example, to learn and controlamplification of input RF signals received by an antenna array, such asthe one or more first antenna arrays 314 or the one or more secondantenna arrays 316, in order to amplify input RF signals, which may havelow-power, without significantly degrading corresponding signal-to-noise(SNR) ratio in the inference phase. In a third example, the centralcloud server 102 may remotely access information (e.g., phase values ofthe input RF signals) associated with elements, such as set of phaseshifters, to train the machine learning model 214, and controladjustment in phase values of the input RF signals, till combined signalstrength value of the received input RF signals, is maximized to designbeams in the inference phase. In a fourth example, the central cloudserver 102 may be configured to train the machine learning model 214with parameters (e.g., amplifier gains, and phase responses) associatedwith the one or more first antenna arrays 314 or the one or more secondantenna arrays 316, and later use learnings in the inference phase tosend control signals to remotely configure or control such parameters.In a fifth example, the central cloud server 102 may be configured toaccess beamforming coefficients from elements of the one or more signalprocessing chains to train the machine learning model 214 and use suchlearnings to configure, and control and adjust beam patterns to and fromeach of the plurality of edge devices 104. In a sixth example, since thecentral cloud server 102 has information associated with elements of oneor more cascaded receiver chains and one or more cascaded transmitterchains of each edge device, the central cloud server 102 may configuredynamic partitioning of a plurality of antenna elements of an antennaarray into a plurality of spatially separated antenna sub-arrays togenerate multiple beams in different directions at the same time or in adifferent time slot. In a seventh example, since the central cloudserver 102 has information associated with elements of one or morecascaded receiver chains and one or more cascaded transmitter chains ofeach edge device, the central cloud server 102 may configure andinstruct an edge device for a suitable adjustment of a power back-off tominimize (i.e. substantially reduce) the impact of interference (echo ornoise signals) and hence only use as much power as needed to achieve lowerror communication with one or more base stations in the uplink or theone or more UEs 106 in the downlink communication. In accordance with anembodiment, the central cloud server 102 may be further configured toconfigure, monitor, and provide management, monitoring and configurationservices to, various layers of each of the plurality of edge devices 104to optimize blocks of radio and perform Radio access networkoptimization to improve coverage, capacity and service quality.

It is known and specified in 3GPP that a radio frame of a 5G NR framestructure may include ten sub-frames, where each sub-frame, includes oneor more slots based on different configurations. In an example, asub-frame may include one slot, where each slot may include 14 symbols(e.g. 14 OFDM symbols). In a case where a sub-frame has two slots, thenthe radio frame has 20 slots. Similarly, in case where the sub-frame hasfour slots, then the radio frame has 40 slots, where the number of OFDMsymbols within a slot is 14. It is also known that NR Time divisionduplexing (TDD) uses flexible slot configuration, where the flexiblesymbol can be configured either for uplink or for downlinktransmissions.

In an implementation, the central cloud server 102 may obtain radioblock information and may access decoded control information from eachof the plurality of edge devices 104. The decoded control informationmay include (or indicates) a periodicity and a downlink/uplink cycleratio, a time division duplex (TDD) pattern, a NR TDD slot format, or aplurality of NR TDD slot formats in a sequence. In accordance with anembodiment, the central cloud server 102 may obtain a physical cellidentifier (PCID), an absolute radio-frequency channel number (ARFCN),and other properties of the plurality of base station of the pluralityof different WCNs 110 through the network (e.g. 4G LTE, 5G NR, Internet,or any other wireless communication network). The central cloud server102 may further receive a channel quality indicator and other channelestimates as a feedback from the plurality of edge devices 104.

In accordance with an embodiment, by virtue of the obtained modeminformation from the plurality of edge devices 104, the central cloudserver 102 may have information of more than one device modem, and thushave holistic information (e.g. an operating behavior) of differentmodems of many edge devices in a geographical area, which can be used totrain the machine learning model 214 and optimize the radiocommunication (e.g. signal propagation) holistically for the entiregeographical area. In an implementation, a software application for eachmodem of an edge device may run on the central cloud server 102 ratherin the modem of an edge device, such as a repeater device. For example,one virtual machine (VM) may be dedicated for one modem of an edgedevice. As the central cloud server 102 has information of more than onedevice modem, it will know about other modems of other edge devices in agiven geographical area, and thus being a high computational resourcecapable device have capability to optimize radio signal propagation andchannel characteristic of the given geographical area, thereby improvingnetwork performance of the plurality of different WCNs 110, andproviding high performance wireless communication for the givengeographical area (and similarly other geographical areas) to improveQoE.

In accordance with an embodiment, the central cloud server 102 may befurther configured to access a Serial Peripheral Interface (SPI) betweena modem and the radio (e.g., the front-end RF section 306) of each ofthe plurality of edge devices 104. The SPI may be a full-duplex businterface used to send data between the control section 304 (e.g., amicrocontroller or DSP) and other peripheral components, such as themodem, for example, a 5G modem, and sensing radar (when present) in anedge device. The SPI interface supports very high speeds, andthroughput, and is suitable for handing a lot of data. In an example,the processing chain parameters 212 may be accesses using access to theSPI.

In an implementation, the processor 202 may be further configured toextract and tag parameters of the processing chain parameters 212 aslearning labels. The obtained sensing information 208 may be consideredas input features, whereas the processing chain parameters 212 may beconsidered as learning labels for the correlation. The processor 202 maybe further configured to execute a mapping of the learning labels withone or more features of the obtained sensing information 208 until theplurality of time-of-day specific uplink and downlink beamalignment-wireless connectivity relationships is established and furtherupdated for the surrounding area of each of the plurality of edgedevices 104.

Similar to the correlation of the obtained sensing information 208 andthe beam alignment information 210, the processor 202 may be furtherconfigured to correlate the processing chain parameters 212 with that ofthe obtained sensing information 208 and the beam alignment information210 for different times-of-day such that the connectivity enhanceddatabase 216 is updated and includes further learned information atholistic level for a plurality of different geographical areasassociated with the plurality of different WCNs 110. The correlationfurther improves QoE and indicates that for a given set of inputfeatures extracted from the sensing information 208, insights areprovided as to what were the processing chain parameters 212 when therewas most suitable (i.e., best) initial access information for a givenedge device to service one or more UEs in its surrounding area, andhence it allows optimal management of network resources including theplurality of edge devices 104 in the inference phase.

In an example, the central cloud server 102 by use of the connectivityenhanced database 216 and the machine learning model 214, and based onthe distribution of the different subset of information from theconnectivity enhanced database 216 to each of the plurality of edgedevices 104 according to a corresponding position of the each of theplurality of edge devices 104, further achieves the following:

(a) reduce time to align to a timing offset of a beam reception at anedge device to a frame structure of a 5G NR radio frame, and allowsuplink and downlink to use complete 5G NR frequency spectrum, but indifferent time slots, where some short time slots are designated foruplink while other time slots are designated for downlink;(b) perform coordination among the edge devices of the plurality of edgedevices 104 for beam forming optimizations for enhanced network coverageand quality of service (QoS);(c) remotely control the phase shifting by controlling the adjustment inphase values of the input RF signals, till combined signal strengthvalue of the received input RF signals, is maximized to design beams inthe inference phase;(d) control amplification of input RF signals, which may have low-power,without significantly degrading corresponding signal-to-noise (SNR)ratio in the inference phase;(e) send control signals to remotely configure or control parameters(e.g., amplifier gains, and phase responses) associated with the one ormore first antenna arrays 314 or the one or more second antenna arrays316;(f) configure and control and adjust beam patterns to and from each ofthe plurality of edge devices 104;(g) remotely configure dynamic partitioning of a plurality of antennaelements of an antenna array into a plurality of spatially separatedantenna sub-arrays to generate multiple beams in different directions atthe same time or in different time slots;(h) configure and instruct an edge device for a suitable adjustment of apower back-off to minimize (i.e., substantially reduce) the impact ofinterference (echo or noise signals) and hence only use as much power asneeded to achieve low error communication with one or more base stationsin the uplink or the one or more UEs 106 in the downlink communication;and(i) optimize blocks of radio and perform Radio access networkoptimization to improve coverage, capacity and service quality ofdifferent geographical areas.

FIG. 3 is a block diagram illustrating different components of anexemplary edge device, in accordance with an embodiment of thedisclosure. FIG. 3 is explained in conjunction with elements from FIGS.1 and 2 . With reference to FIG. 3 , there is shown a block diagram 300of the first edge device 104A. The first edge device 104A may include acontrol section 304 and a front-end radio frequency (RF) section 306.The control section 304 may include a control circuitry 308 and a memory310. The control section 304 may be communicatively coupled to thefront-end RF section 306. The front-end RF section 306 may includefront-end RF circuitry 312 and a plurality of antenna arrays, such asone or more first antenna arrays 314 and one or more second antennaarrays 316.

The first edge device 104A includes suitable logic, circuitry, andinterfaces that may be configured to communicate with one or morenetwork nodes, such as one or more base stations of the plurality ofbase stations 108, another edge device of the plurality of edge devices104, and user equipment (UEs). In accordance with an embodiment, thefirst edge device 104A may support multiple and a wide range offrequency spectrum, for example, 2G, 3G, 4G, 5G, and 6G (includingout-of-band frequencies). The first edge device 104A is one of anXG-enabled edge repeater device, an XG-enabled relay device, anXG-enabled small-cell, or an XG-enabled user equipment (UE) controlledby the central cloud server 102, where the term “XG” refers to 5G or 6G.Other examples of the first edge device 104A may include, but is notlimited to, a 5G wireless access point, an evolved-universal terrestrialradio access-new radio (NR) dual connectivity (EN-DC) device, aMultiple-input and multiple-output (MIMO)-capable repeater device, or acombination thereof.

The control circuitry 308 may be communicatively coupled to the memory310 and the front-end RF section 306 including the front-end RFcircuitry 312, the one or more first antenna arrays 314, and the one ormore second antenna arrays 316. The control circuitry 308 may beconfigured to execute various operations of the first edge device 104A.The control circuitry 308 may be configured to control variouscomponents of the front-end RF section 306. The first edge device 104Amay be a programmable device, where the control circuitry 308 mayexecute instructions stored in the memory 310. Examples of theimplementation of the control circuitry 308 may include, but are notlimited to an embedded processor, a microcontroller, a specializeddigital signal processor (DSP), a Reduced Instruction Set Computing(RISC) processor, an Application-Specific Integrated Circuit (ASIC)processor, a Complex Instruction Set Computing (CISC) processor, and/orother processors, or state machines.

The memory 310 may be configured to store the subset of informationobtained from the central cloud server 102, where the subset ofinformation specifies a plurality of time-of-day specific uplink anddownlink beam alignment-wireless connectivity relationships for asurrounding area specific to the first edge device 104A. Examples of theimplementation of the memory 310 may include, but not limited to, arandom access memory (RAM), a dynamic random access memory (DRAM), astatic random access memory (SRAM), a processor cache, a thyristorrandom access memory (T-RAM), a zero-capacitor random access memory(Z-RAM), a read only memory (ROM), a hard disk drive (HDD), a securedigital (SD) card, a flash drive, cache memory, and/or othernon-volatile memory. It is to be understood by a person having ordinaryskill in the art that the control section 304 may further include one ormore other components, such as an analog to digital converter (ADC), adigital to analog (DAC) converter, a cellular modem, and the like, knownin the art, which are omitted for brevity.

The front-end RF circuitry 312 includes receiver circuitry andtransmitter circuitry. The receiver circuitry is coupled to the one ormore receiving antenna arrays, such as one of the one or more firstantenna arrays 114 or the one or more second antenna arrays 116, or maybe a part of the receiver chain. The transmitter circuitry may becoupled to the one or more transmitting antenna arrays, such as the oneof the one or more first antenna arrays 114 or the one or more secondantenna arrays 116 in an implementation. The front-end RF circuitry 312supports millimeter wave (mmWave) communication as well communication ata sub 6 gigahertz (GHz) frequency.

Each of the one or more first antenna arrays 114 and the one or moresecond antenna arrays 116 may be one of an XG phased-array antennapanel, an XG-enabled antenna chipset, an XG-enabled patch antenna array,or an XG-enabled servo-driven antenna array, where the “XG” refers to 5Gor 6G. Examples of implementations of the XG phased-array antenna panelinclude, but is not limited to, a linear phased array antenna, a planarphased array antenna, a frequency scanning phased array antenna, adynamic phased array antenna, and a passive phased array antenna.

In operation, in accordance with an embodiment, the control circuitry308 may be configured to capture sensing information of a surrounding ofthe first edge device 104A. The control circuitry 308 may be configuredto periodically sense its surroundings and communicate the sensedinformation, such as the sensing information 208, to the central cloudserver 102. Based on where the first edge device 104A is deployed, forexample, whether deployed at a fixed location or as a portable device,for example, mounted on a vehicle or as a portable repeater device, thefirst edge device 104A may use its own sensing mechanism, such as asensing radar, to sense its surrounding environment, utilize externalsensing devices, or utilize a combination of both. In an implementation,when deployed at the fixed location, the control circuitry 308 mayutilize the sensing radar and one or more image-capture devices to mapits surrounding three-dimensional (3D) environment to generate a 3Denvironmental representation. The 3D environmental representation mayindicate movable and immobile physical structures in the surroundingarea of the first edge devices 104A. In some implementations, whendeployed at a vehicle, the first edge device 104A may be configured toutilize external sensing devices, such as Lidar, camera, accelerometer,GNSS, gyroscope, or IoT devices (e.g. video surveillance devices,roadside sensor systems for measuring speed, local road conditions,local traffic, and the like) located within its communication range toacquire sensing information 208 from such external devices. Otherexamples of the sensing information 208 may include, but not limited to,a 2D position of the first edge device 104A, a 3D position (includingelevation if deployed at a fixed location like a pole), a location ofthe one or more UEs 106 in the motion state or in the stationary statein the surrounding area, a moving direction of different UEs, atime-of-day, traffic information, road information, constructioninformation, traffic light information, nearby bridges, location ofreflective objects in the surrounding area, weather information, adistance of the first edge device 104A from one or more UEs 106 withinits range, distance of the first edge device 104A from its surroundingobjects, such as other vehicles, buildings, or edges of a building,distance of one or more serving base stations of the plurality of basestations 108, trees, and other immobile physical structures (such asreflective objects) or other mobile objects, or any change detected inthe surrounding area of the first edge device 104A. The controlcircuitry 308 may be further configured to periodically communicatesensing information 208 to the central cloud server 102.

The control circuitry 308 may be further configured to periodicallycommunicate beam alignment information 210 to the central cloud server102. The beam alignment information 210 may comprise one or more of atransmit (Tx) beam information associated with the first edge device104A, a receive (Rx) beam information associated with the first edgedevice 104A, a Physical Cell Identity (PCID) currently used by the firstedge device 104A, an absolute radio-frequency channel number (ARFCN)used by the first edge device 104A, and a signal strength informationassociated with each of Tx beam and the Rx beam of the first edge device104A. All such measurements and feedback are sent to the central cloudserver 102 for learning.

In accordance with an embodiment, the sensing information 208 and thebeam alignment information 210 obtained by the central cloud server 102from the edge device and other edge devices of a plurality of edgedevices 104 is correlated by the central cloud server 102 for differenttimes-of-day such that a connectivity enhanced database 216 is generatedthat specifies a plurality of time-of-day specific uplink and downlinkbeam alignment-wireless connectivity relationships for a surroundingarea of each of the plurality of edge devices 104 independent of aplurality of different WCNs 110.

The control circuitry 308 may be configured to obtain a subset ofinformation from the central cloud server 102 according to a position ofthe first edge device 104A, where the subset of information specifies aplurality of time-of-day specific uplink and downlink beamalignment-wireless connectivity relationships for a surrounding areaspecific to the first edge device 104A. The control circuitry 308 may befurther configured to receive a corresponding connection request fromone or more UEs 106. The connection request may be received via anout-of-band communication, such as Wi-Fi™, BLUETOOTH™, Li-Fi, a sidelinkrequest (e.g., LTE sidelink, 5G New Radio (NR) sidelink, NR C-V2Xsidelink), a vehicle-to-infrastructure (V2I) request, a personal areanetwork (PAN) connection, or other out-of-band connection requests. Thecontrol circuitry 308 may be further configured to identity the one ormore UEs 106 based on the connection request. Based on the obtainedsubset of information and the corresponding connection request, thecontrol circuitry 308 may be further configured to service one or UEs106 in the surrounding area bypassing an initial access-search on thefirst edge device 104A. The first edge device 104A is independent of aplurality of different WCNs 110 such that any one of the plurality ofdifferent WCNs 110 is used to service a specific UE in accordance withan association of the specific UE to a specific wireless carriernetwork. Thus, a consumer, such as the first UE 106A, has is providedwith the capability to choose which WCN (i.e. which service provider)they like to connect to, and this is enabled from the cloud, such as thecentral cloud server 102. The central cloud server 102 transmits aspecific initial access information (optimal initial access information)associated with a WCN, such as the first WCN 110A, to the first edgedevice 104A, where such specific initial access information is used bythe first edge device 104A to establish wireless connectivity by passingconventional initial-access search. Hence, beneficially, a consumer of aUE, such as the first UE 106A, subscribed to the first WCN 110A canrequest the first edge device 104A in the connection request to relay anRF signal of the first WCN 110A, and if the consumer of the first UE106A is subscribed to the second WCN 110B, then the first UE 106A canrequest the first edge device 104A, to relay an RF signal of the secondWCN 110B. Additionally, and advantageously, as the plurality oftime-of-day specific uplink and downlink beam alignment-wirelessconnectivity relationships for the surrounding area of the first edgedevice 104A is independent of the plurality of different WCNs 110, thecomplexity and the initial access latency is significantly reduced asthe standard beam sweeping operation in the initial access phase isbypassed and is not required to be performed at the one or more UEs 106and the first edge device 104A which in turn improves networkperformance and reduces additional signaling load (due to standardinitial-access search) on associated WCNs of the plurality of differentWCNs 110.

In yet another aspect of the disclosure, one or more of the plurality ofedge devices may be UEs controlled by the central cloud server 102.Thus, due to the awareness of a physical location of a given edge device(in this case, a UE), the edge device may be configured to obtain thewireless connectivity enhanced information that includes a specificinitial access information for the given edge device (i.e. a UE) tobypass initial access search at the given edge device (i.e. the UE), andfurther may be connected (i.e., attached) to a base station (e.g., agNB) directly (or via a nearby small cell or CPE) specified in theobtained specific initial access information from the central cloudserver 102 with reduced latency as compared to standard gNB handovertime. Thus, arbitrated between the central cloud server 102 and thegiven edge device (i.e. the UE), alleviates other network nodes (such asa CPE, or a small cell present in the vicinity of the UE) from thesecomplex functions, thereby simplifying their beam forming design andconsequently lower cost of infrastructure.

In some scenarios, one or more of the plurality of edge devices may beCPEs. In such a case, a given edge device, such as the second edgedevice 104B, may be configured to obtain the wireless connectivityenhanced information that includes a specific initial access informationfor given edge device (in this scenario, a CPE), where the specificinitial access information may specify to connect to a nearby small cellto service a UE for high performance communication. Thus, arbitratedbetween the central cloud server 102 and the given edge device (due tothe cloud awareness of the physical location of the UE as well as theCPE), alleviates the CPE from these complex functions, for example,location tracking of the UE, thereby simplifying its beam forming designand consequently lowering cost.

FIGS. 4A, 4B, and 4C illustrate exemplary scenarios for implementationof the communication system and method for central cloud server and edgedevices assisted high speed low-latency wireless connectivity, inaccordance with an embodiment of the disclosure. FIGS. 4A, 4B, and 4Care explained in conjunction with elements from FIGS. 1, 2, and 3 . Withreference to FIGS. 4A, 4B, and 4C, there is shown a first vehicle 402, asecond vehicle 404, a plurality of repeater devices, such as repeaterdevices 406A and 406B, and a plurality of base stations, such as gNBs408A, 408B, 408C, and 408D, and the central cloud server 102 (FIGS. 1and 2 ). The gNBs 408A, 408C, and 408D may be of the first WCN 110A of afirst service provider and the gNBs 408B may be of the second WCN 110Bof a second service provider. In a first implementation, the firstvehicle 402 may correspond to a 5G-enabled UE controlled by the centralcloud server 102. In an example, the first vehicle 402 may have anapplication installed in it (e.g. installed in an in-vehicleinfotainment system) which is communicatively coupled to the centralcloud server 102 to receive its services. Alternatively, in a secondimplementation, the first vehicle 402 may include a UE, for example, asmartphone or an in-vehicle device, which has the application installedin it, and which is communicatively coupled to the central cloud server102 to receive its services. For the sake of brevity, some exemplaryfunctions of the central cloud server 102 and the method for centralcloud server 102 and edge devices assisted high speed low-latencywireless connectivity, is described by taking an example of the firstimplementation. However, it is to be understood that functions describedfor the first vehicle 402 are also applicable for the secondimplementation, i.e., functions of the UE within a vehicle, such as thefirst vehicle 402, without limiting the scope of the disclosure.

With reference to FIG. 4A, there is shown a first exemplary scenario400A, in which the first vehicle 402 and the second vehicle 404 are inmotion. In this case, the first vehicle 402 may be a semi-autonomous oran autonomous vehicle. The first vehicle 402 may be attached to the gNB408A of the first WCN 110A while in motion. In some implementations, thefirst vehicle 402 may be configured to communicate sensing informationin real time or near real time to the central cloud server 102. In someimplementations, the first vehicle 402 may be configured to communicatesensing information to a first inference server that may be deployednearest to the current location of the first vehicle 402. There may beseveral inference servers deployed at different locations servingdifferent geographical areas, which may be communicatively coupled tothe central cloud server 102. The decision to whether to communicate thesensing information directly to the central cloud server 102 or to thenearest deployed inference server may be based on a configured settingon the application and/or based on an amount or a type of data that isto be communicated. This further provides a hybrid computing capabilitybased on a user preference (e.g., as opt-in or opt-out features providedto premium users) to the communication system including the centralcloud server 102 and the method of the present disclosure. The secondvehicle may also be attached to the gNB 408A. In the first exemplaryscenario 400A, the central cloud server 102 (or the first inferenceserver based on the subset of information communicated previously by thecentral cloud server 102) may be configured to obtain the sensinginformation and extract features from the sensing information anddetermine that no handover is required for the first vehicle 402 in areal time or a near time. As a result of the machine learning model 214and the connectivity enhanced database 216 of the central cloud server102, it is immediately ascertained that for the extracted features(e.g., a time-of day, a current position of the first vehicle 402, adistance of the first vehicle 402 from the gNB 408A, a distance of thefirst vehicle 402 from the repeater devices 406A and 406B, a current 3Denvironment representation that indicates any possibility of signalblockages or fading, road condition, traffic information, and a currentweather condition), the performance state of a wireless connection ofthe first vehicle 402 is greater than a threshold performance value, andthere is no need for any handover. There is no need to do any signalmeasurements at this point because of the low-latency connectivityenhanced database 216, which can holistically handle multi-dimensionalinput features.

With reference to FIG. 4B, there is shown a second exemplary scenario400B in continuation to the first exemplary scenario 400A. In the secondexemplary scenario 400B, the first vehicle 402 and the second vehicle404 further move ahead, as shown. The first vehicle 402 may further sendsensing information to the central cloud server 102 (or the firstinference server based on a selected setting on the installedapplication). However, in this case, the central cloud server 102 (orthe first inference server) may be further configured to determine thata handover is required for the first vehicle 402, based on the recentlyreceived sensing information, which indicates that some mobile object(i.e., the second vehicle 404) may be blocking a 5G signal from the gNB408A. Accordingly, the central cloud server 102 (or the first inferenceserver) selects an appropriate repeater device, i.e., the repeaterdevice 406B, to communicate wireless connectivity enhanced informationincluding a specific initial access information to the repeater device406B to bypass the initial access-search on the repeater device 406B andthe first vehicle 402. In this case, the repeater device 406B may beattached to the gNB 408B of the second WCN 110B initially, but quicklyswitches over to the gNB 408C of the first WCN 110A based on thespecific initial access information (e.g. a given donor beam index, PCIDof gNB 408C, and related ARFCN) received from the central cloud server102. Thus, the repeater device 406B may be independent of the pluralityof different WCNs 110, such as the first WCN 110A and the second WCN110B. The specific initial access information may further indicate toselect a particular service side beam index, e.g., a beam index #19 outof 0-63 and a particular beam configuration based on time-of-day andother sensing information, to service the first vehicle 402 bypassingthe initial access search at the repeater device 406B as well as thefirst vehicle 402, where the handover time is much lesser than thestandard average mm-wave gNB handover time under same scenarios, such assame cell radius and vehicle travelling speed.

With reference to FIG. 4C, there is shown a third exemplary scenario400C in continuation to the second exemplary scenario 400B. In the thirdexemplary scenario 400C, the first vehicle 402 and the second vehicle404 further move ahead, where first vehicle 402 is about to move beyonda coverage area of the gNB 408C. The first vehicle 402 (i.e., a5G-enabled UE controlled by the central cloud server 102) may furthersend updated sensing information to the central cloud server 102 (or thefirst inference server). Based on the updated sensing information, thecentral cloud server 102 (or the first inference server) may predictthat will be no deployed repeater devices or other network nodes (suchas a small cell, an RSU, etc.) that may be in a communication range ofthe first vehicle 402 in the travel path based on a moving direction andspeed of the first vehicle 402 and that a handover to a new gNB, such asthe gNB 408D of the first WCN 110A, will need to be executed by thefirst vehicle 402 as the first vehicle 402 moves beyond the coveragearea of the gNB 408C. Thus, the central cloud server 102 (or the firstinference server) may be further configured to communicate a wirelessconnectivity enhanced information including a new specific initialaccess information to the first vehicle 402 to bypass the initialaccess-search on the first vehicle 402 and quickly attach to the gNB408D, say less than one or two seconds. The second vehicle 404, may be aconventional vehicle, and may not be a known user to the central cloudserver 102 (or may not be communicatively coupled to the central cloudserver 102 to receive its services), and thus may need to performstandard initial-access search to attach to the gNB 408D, which may takea standard time (e.g. the average mmWave gNB handover time is on theorder of 10-20 sec, assuming ˜500 m cell radius (i.e. coverage area) andtravelling speed of 50 MPH). For example, the second vehicle 404 mayneed to perform following four beam management operations: a) Beamsweeping, where an exhaustive scanning of a spatial area with a set ofbeams transmitted and received needs to done; b) Beam measurement, wheresignal quality, such as received power (RSRP), Signal to Interferenceplus Noise Ratio (SINR), of the received beam of RF signals, may need tobe executed; c) Beam determination, where an optimal beam (or set ofbeams) may be selected for establishing directional communications; andd) Beam reporting, it is reported to network of the signal quality andon the decisions made in the previous phase. The first vehicle 402 byvirtue of the obtained wireless connectivity enhanced information thatincludes optimal initial access information is able to bypass theinitial access-search and reduce signaling overhead usually incurred bynetwork processes by avoiding many of such standard beam managementoperations on the first vehicle 402 without any adverse impact whilestill maintaining QoE with high reliability and consistency.

FIGS. 5A and 5B collectively is a flowchart that illustrates a methodfor a central cloud server and edge devices assisted high speedlow-latency wireless connectivity for high performance communication, inaccordance with an embodiment of the disclosure. FIGS. 5A and 5B areexplained in conjunction with elements from FIGS. 1, 2, 3, 4A and 4B.With reference to FIGS. 5A and 5B, there is shown a flowchart 500comprising exemplary operations 502 through 518. The operations of themethod depicted in the flowchart 500 may be implemented in the centralcloud server 102 (FIG. 1 ).

At 502, sensing information 208 may be periodically obtained from theplurality of edge devices 104. The processor 202 may be configured toperiodically obtain sensing information 208 from the plurality of edgedevices 104. The sensing information 208 may comprise a position of eachof the plurality of edge devices 104, a location of the one or more UEs106 in the motion state or in the stationary state in the surroundingarea of each of the plurality of edge devices 104, a moving direction ofthe one or more UEs 106, a time-of-day, traffic information, roadinformation, construction information, traffic light information, andweather information.

At 504, a distance of each of the plurality of edge devices 104 from oneor more UEs 106 and other movable and immobile physical structures inthe surrounding area of each of the plurality of edge devices 104 may bedetermined. The processor 202 may be further configured to determinesuch distance.

At 506, supplementary information may be generated as insights based oncross-correlation of data points of the sensing information 208. Theprocessor 202 may be further configured to generate the supplementaryinformation as insights based on cross-correlation of data points of thesensing information 208.

At 508, beam alignment information 210 may be periodically obtained fromthe plurality of edge devices 104. The processor 202 may be furtherconfigured to periodically obtain beam alignment information 210 fromthe plurality of edge devices 104. The beam alignment information 210received by the central cloud server 102 from the plurality of edgedevices 104 during a training phase may comprise one or more of atransmit (Tx) beam information, a receive (Rx) beam information, aPhysical Cell Identity (PCID), and an absolute radio-frequency channelnumber (ARFCN), and a signal strength information associated with eachof Tx beam and the Rx beam of the plurality of edge devices 104.

At 510, the weather information may be utilized to determine one or morechanges in a performance state of each of the plurality of edge devices104 in servicing the one or more UEs 106 in its surrounding area indifferent weather conditions. The processor 202 may be furtherconfigured to utilize the weather information to determine one or morechanges in a performance state of each of the plurality of edge devices104 in servicing the one or more UEs 106 in its surrounding area indifferent weather conditions.

At 512, the obtained sensing information 208 and the beam alignmentinformation 210 may be correlated for different times-of-day such that aconnectivity enhanced database 216 is generated that specifies aplurality of time-of-day specific uplink and downlink beamalignment-wireless connectivity relationships for a surrounding area ofeach of the plurality of edge devices 104 independent of the pluralityof different WCNs 110. The processor 202 may be further configured tocorrelate the obtained sensing information 208 and the beam alignmentinformation 210 for different times-of-day. In an implementation, theoperation 512 may include sub-operations 512A and 5128.

At 512A, parameters of the beam alignment information 210 may beextracted and tagged as learning labels. The processor 202 may befurther configured to extract and tag parameters of the beam alignmentinformation 210 as learning labels.

At 5128, a mapping of the learning labels may be executed with one ormore features of the obtained sensing information 208 until theplurality of time-of-day specific uplink and downlink beamalignment-wireless connectivity relationships is established for thesurrounding area of each of the plurality of edge devices 104. Theprocessor 202 may be further configured to execute the mapping of thelearning labels with one or more features of the obtained sensinginformation 208 until the plurality of time-of-day specific uplink anddownlink beam alignment-wireless connectivity relationships isestablished for the surrounding area of each of the plurality of edgedevices 104.

At 514, a different subset of information may be distributed from theconnectivity enhanced database 216 to each of the plurality of edgedevices 104 according to a corresponding position of the each of theplurality of edge devices 104, where the different subset of informationmay cause each of the plurality of edge devices 104 to service one ormore UEs 106 in a motion state or in a stationary state in itssurrounding area independent of the plurality of different WCNs 110bypassing an initial access-search on the corresponding edge device,such as the first edge device 104A. The processor 202 may be furtherconfigured to distribute the different subset of information from theconnectivity enhanced database 216 to each of the plurality of edgedevices 104 according to the corresponding position of the each of theplurality of edge devices 104.

At 516, it may be determined, based on position information of the firstedge device 104A, whether a handover is required, and if so, communicatewireless connectivity enhanced information including a specific initialaccess information to the first edge device 104A to bypass the initialaccess-search on the first edge device 104A. The processor 202 may befurther configured to determine, based on the position information ofthe first edge device 104A, whether the handover is required, and if so,communicate wireless connectivity enhanced information including thespecific initial access information to the first edge device 104A tobypass the initial access-search on the first edge device 104A.

At 518, it may be determined that no handover is required for the firstedge device 104A when a performance state of a wireless connection ofthe first UE 106A is greater than a threshold performance value. Theprocessor 202 may be further configured to determine that no handover isrequired for the first edge device 104A.

FIG. 6 is a flowchart that illustrates a method for a central cloudserver assisted high speed low-latency wireless connectivity for highperformance communication, in accordance with an embodiment of thedisclosure. FIGS. 6A and 6B are explained in conjunction with elementsfrom FIGS. 1, 2, 3, 4A, and 4B. With reference to FIGS. 6A and 6B, thereis shown a flowchart 600 comprising exemplary operations 602 through612. The operations of the method depicted in the flowchart 600 may beimplemented in an edge device, such as the first edge device 104A (FIG.1 ).

At 602, sensing information of a surrounding area of the first edgedevice 104A may be captured. The sensing information captured by thefirst edge device 104A is described in details, for example, in FIG. 3 .At 604, sensing information 208 may be periodically communicated to thecentral cloud server 102.

At 606, beam alignment information 210 may be periodically communicatedto the central cloud server 102. In accordance with an embodiment, thesensing information 208 and the beam alignment information 210 obtainedby the central cloud server 102 from the edge device and other edgedevices of a plurality of edge devices 104 is correlated by the centralcloud server 102 for different times-of-day such that a connectivityenhanced database 216 is generated that specifies a plurality oftime-of-day specific uplink and downlink beam alignment-wirelessconnectivity relationships for a surrounding area of each of theplurality of edge devices 104 independent of a plurality of differentWCNs 110.

At 608, a subset of information may be obtained from the central cloudserver 102 according to a position of the first edge device 104A, wherethe subset of information specifies a plurality of time-of-day specificuplink and downlink beam alignment-wireless connectivity relationshipsfor a surrounding area specific to the first edge device 104A.

At 610, a corresponding connection request may be received from one ormore UEs 106. The connection request may be received via an out-of-bandcommunication, such as Wi-Fi™, BLUETOOTH™, Li-Fi, a sidelink request(e.g. LTE sidelink, 5G New Radio (NR) sidelink, NR C-V2X sidelink), avehicle-to-infrastructure (V2I) request, a personal area network (PAN)connection, or other out-of-band connection requests. The one or moreUEs 106 may be identified as priority users based on the connectionrequest in order to prioritize servicing the one or more UEs 106.

At 612, based on the obtained subset of information and thecorresponding connection request, one or UEs 106 in the surrounding areamay be serviced bypassing an initial access-search on the first edgedevice 104A, where the first edge device 104A is independent of aplurality of different WCNs 110 such that any one of the plurality ofdifferent WCNs 110 is used to service a specific UE in accordance withan association of the specific UE to a specific wireless carriernetwork.

Various embodiments of the disclosure may provide a non-transitorycomputer-readable medium having stored thereon, computer implementedinstructions that when executed by a computer causes the computer toexecute operations to periodically obtain sensing information from aplurality of edge devices 104 and periodically obtain beam alignmentinformation from the plurality of edge devices 104. The operations alsoinclude correlating the obtained sensing information and the beamalignment information for different times-of-day such that aconnectivity enhanced database 216 is generated that specifies aplurality of time-of-day specific uplink and downlink beamalignment-wireless connectivity relationships for a surrounding area ofeach of the plurality of edge devices 104 independent of a plurality ofdifferent WCNs 110. The operation further includes distributing adifferent subset of information from the connectivity enhanced database216 to each of the plurality of edge devices 104 according to acorresponding position of the each of the plurality of edge devices 104.The different subset of information causes each of the plurality of edgedevices 104 to service one or more user equipment (UEs) 106 in a motionstate or in a stationary state in its surrounding area independent ofthe plurality of different WCNs 110 and bypassing an initialaccess-search on the corresponding edge device.

Various embodiments of the disclosure may include a central cloud server102 (FIG. 1 ). The central cloud server 102 comprises a processor 202configured to periodically obtain sensing information from a pluralityof edge devices 104. The processor 202 may be further configured toperiodically obtain beam alignment information from the plurality ofedge devices 104. The processor 202 may be further configured tocorrelate the obtained sensing information and the beam alignmentinformation for different times-of-day such that a connectivity enhanceddatabase 216 is generated that specifies a plurality of time-of-dayspecific uplink and downlink beam alignment-wireless connectivityrelationships for a surrounding area of each of the plurality of edgedevices 104 independent of a plurality of different wireless carriernetworks 110. The processor 202 may be further configured to distributea different subset of information from the connectivity enhanceddatabase 216 to each of the plurality of edge devices 104 according to acorresponding position of the each of the plurality of edge devices 104,wherein the different subset of information causes each of the pluralityof edge devices 104 to service one or more user equipment (UEs) 106 in amotion state or in a stationary state in its surrounding areaindependent of the plurality of different WCNs 110 bypassing an initialaccess-search on the corresponding edge device.

Various embodiments of the disclosure may include a first edge device104A, for example, a relay device, a small cell, or an edge repeaterdevice. The first edge device 104A comprises control circuitry 308configured to obtain a subset of information from a central cloud server102 according to a position of the first edge device 104A, wherein thesubset of information specifies a plurality of time-of-day specificuplink and downlink beam alignment-wireless connectivity relationshipsfor a surrounding area specific to the first edge device 104A. Thecontrol circuitry 308 may be further configured to receive acorresponding connection request from one or more user equipment (UEs)106. Based on the obtained subset of information and the correspondingconnection request, the control circuitry 308 may be further configuredto service one or more user equipment (UEs) 106 in the surrounding areabypassing an initial access-search on the first edge device 104A,wherein the first edge device 104A is independent of a plurality ofdifferent wireless carrier networks (WCNs) 110 such that any one of theplurality of different wireless carrier networks 110 is used to servicea specific UE in accordance with an association of the specific UE to aspecific wireless carrier network.

While various embodiments described in the present disclosure have beendescribed above, it should be understood that they have been presentedby way of example, and not limitation. It is to be understood thatvarious changes in form and detail can be made therein without departingfrom the scope of the present disclosure. In addition to using hardware(e.g., within or coupled to a central processing unit (“CPU”),microprocessor, micro controller, digital signal processor, processorcore, system on chip (“SOC”) or any other device), implementations mayalso be embodied in software (e.g. computer readable code, program code,and/or instructions disposed in any form, such as source, object ormachine language) disposed for example in a non-transitorycomputer-readable medium configured to store the software. Such softwarecan enable, for example, the function, fabrication, modeling,simulation, description and/or testing of the apparatus and methodsdescribe herein. For example, this can be accomplished through the useof general program languages (e.g., C, C++), hardware descriptionlanguages (HDL) including Verilog HDL, VHDL, and so on, or otheravailable programs. Such software can be disposed in any knownnon-transitory computer-readable medium, such as semiconductor, magneticdisc, or optical disc (e.g., CD-ROM, DVD-ROM, etc.). The software canalso be disposed as computer data embodied in a non-transitorycomputer-readable transmission medium (e.g., solid state memory or anyother non-transitory medium including digital, optical, analog-basedmedium, such as removable storage media). Embodiments of the presentdisclosure may include methods of providing the apparatus describedherein by providing software describing the apparatus and subsequentlytransmitting the software as a computer data signal over a communicationnetwork including the internet and intranets.

It is to be further understood that the system described herein may beincluded in a semiconductor intellectual property core, such as amicrocontroller (e.g., embodied in HDL) and transformed to hardware inthe production of integrated circuits. Additionally, the systemdescribed herein may be embodied as a combination of hardware andsoftware. Thus, the present disclosure should not be limited by any ofthe above-described exemplary embodiments but should be defined only inaccordance with the following claims and their equivalents.

What is claimed is:
 1. A central cloud server, comprising: a primarystorage configured to store a connectivity enhanced database thatspecifies a plurality of time-of-day specific uplink and downlink beamalignment-wireless connectivity relationships for a surrounding area ofeach of a plurality of edge devices; and a processor configured to:obtain sensing information from the plurality of edge devices in realtime or near real time, wherein the sensing information comprises atleast position information of each of the plurality of edge devices andweather information in the surrounding area of each of the plurality ofedge devices; utilize the weather information to determine one or morechanges in a performance state of each of the plurality of edge devicesin servicing one or more user equipment (UEs) in the surrounding area ofeach of the plurality of edge devices in different weather conditions;and cause one or more edge devices of the plurality of edge devices toselect a beam configuration or a radiation pattern in accordance withthe weather information obtained as a part of the sensing informationand the determined one or more changes in the performance state of eachof the plurality of edge devices.
 2. The central cloud server accordingto claim 1, wherein the processor is further configured to select theone or more edge devices in advance to mitigate signal losses in thedifferent weather conditions to serve the one or more UEs in thesurrounding area of each of the plurality of edge devices and toidentify one or more signal transmission paths to reach to the one ormore UEs via the selected one or more edge devices.
 3. The central cloudserver according to claim 1, wherein the processor is further configuredto learn a correlation among each of the different weather conditions, asignal power level at each of the plurality of edge devices, and theperformance state of each of the plurality of edge devices in servicingthe one or more UEs in the surrounding area of each of the plurality ofedge devices.
 4. The central cloud server according to claim 1, whereinthe plurality of time-of-day specific uplink and downlink beamalignment-wireless connectivity relationships for the surrounding areaof each of the plurality of edge devices is specified in theconnectivity enhanced database independent of a plurality of differentwireless carrier networks (WCNs).
 5. The central cloud server accordingto claim 1, wherein the processor is further configured to: determine,based on the position information of a first edge device of theplurality of edge devices, that a handover is required for the firstedge device; and communicate wireless connectivity enhanced informationincluding a specific initial access information from the connectivityenhanced database to the first edge device to bypass an initialaccess-search on the first edge device.
 6. The central cloud serveraccording to claim 5, wherein the processor is further configured todetermine that no handover is required for the first edge device when aperformance state of a wireless connection of the first edge device isgreater than a threshold performance value.
 7. The central cloud serveraccording to claim 5, wherein the processor is further configured tosend a subset of information specific to the first edge device from theconnectivity enhanced database based on a current position of the firstedge device, wherein the subset of information specific to the firstedge device includes at least one of the plurality of time-of-dayspecific uplink and downlink beam alignment-wireless connectivityrelationships for a current surrounding area of the first edge device.8. The central cloud server according to claim 1, wherein the processoris further configured to distribute a different subset of informationfrom the connectivity enhanced database to each of the plurality of edgedevices according to a corresponding position of the each of theplurality of edge devices, and wherein the different subset ofinformation may cause each of the plurality of edge devices to servicethe one or more UEs in a motion state or in a stationary state in itssurrounding area bypassing an initial access-search on the correspondingedge device.
 9. The central cloud server according to claim 1, whereinthe processor is further configured to control a switchover of a userequipment (UE) of the one or more UEs from one edge device to anotheredge device to maintain a consistent cellular connectivity and datathroughput in multi-gigabit data rate as the UE moves from onegeographical area to another geographical area.
 10. The central cloudserver according to claim 1, wherein the processor is further configuredto: obtain beam alignment information from the plurality of edgedevices; and correlate the obtained sensing information and the beamalignment information for different times-of-day such that theconnectivity enhanced database is generated and stored in the primarystorage.
 11. The central cloud server according to claim 10, wherein thebeam alignment information received by the central cloud server from theplurality of edge devices during a training phase comprises one or moreof: a transmit (Tx) beam information, a receive (Rx) beam information, aPhysical Cell Identity (PCID), and an absolute radio-frequency channelnumber (ARFCN), and a signal strength information associated with eachof Tx beam and Rx beam of the plurality of edge devices.
 12. The centralcloud server according to claim 10, wherein the processor is furtherconfigured to: obtain processing chain parameters from the plurality ofedge devices; and correlate the processing chain parameters further withthe obtained sensing information and the beam alignment information forthe different times-of-day such that the connectivity enhanced databaseis updated for a plurality of different geographical areas associatedwith a plurality of different wireless carrier networks (WCNs).
 13. Thecentral cloud server according to claim 12, wherein the processing chainparameters comprises information associated with elements of one or morecascaded receiver chains and one or more cascaded transmitter chains ofeach edge device of the plurality of edge devices, radio blocksinformation, and modem information of the plurality of edge devices. 14.The central cloud server according to claim 1, wherein the processor isfurther configured to send control signals to remotely configure orcontrol parameters of amplifier gains and phase responses associatedwith one or more first antenna arrays or one or more second antennaarrays of each edge device of the plurality of edge devices.
 15. Thecentral cloud server according to claim 1, wherein the sensinginformation further comprises two or more of: a moving direction of avehicle, a time-of-day, and traffic information.
 16. The central cloudserver according to claim 15, wherein the sensing information furthercomprises two or more of: a location of one or more user equipment (UEs)in a motion state or in a stationary state in the surrounding area ofeach of the plurality of edge devices, a moving direction of the one ormore UEs, road information, construction information, and traffic lightinformation.
 17. The central cloud server according to claim 1, whereineach of the plurality of edge devices is one of: an edge repeaterdevice, a small cell, a customer premise equipment (CPE), a Road-SideUnit (RSU) device, a vehicle, a relay device, or a user equipment (UE)controlled by the central cloud server.
 18. A method for a central cloudserver and edge devices assisted high speed, low-latency wirelessconnectivity, the method comprising: in the central cloud server:storing a connectivity enhanced database that specifies a plurality oftime-of-day specific uplink and downlink beam alignment-wirelessconnectivity relationships for a surrounding area of each of a pluralityof edge devices; obtaining sensing information from the plurality ofedge devices in real time or near real time, wherein the sensinginformation comprises at least position information of each of theplurality of edge devices and weather information in the surroundingarea of each of the plurality of edge devices; utilizing the weatherinformation to determine one or more changes in a performance state ofeach of the plurality of edge devices in servicing one or more userequipment (UEs) in the surrounding area in different weather conditions;and causing one or more edge devices of the plurality of edge devices toselect a beam configuration or a radiation pattern in accordance withthe weather information obtained as a part of the sensing informationand the determined one or more changes in the performance state of eachof the plurality of edge devices.
 19. The method according to claim 18,further comprising selecting the one or more edge devices in advance tomitigate signal losses in the different weather conditions to serve theone or more UEs in the surrounding area of each of the plurality of edgedevices and to identify one or more signal transmission paths to reachto the one or more UEs via the selected one or more edge devices. 20.The method according to claim 18, further comprising learning acorrelation among each of the different weather conditions, a signalpower level at each of the plurality of edge devices, and theperformance state of each of the plurality of edge devices in servicingthe one or more UEs in the surrounding area.